Glioma comprises about 40% of all primary brain tumors accounting for as many as 26,000 U.S. and European deaths annually. Inherited susceptibility plays a role with 2-fold increased risk of glioma among first-degree relatives of glioma cases. We initiated a multi-center, international linkage study: Genetic Epidemiology of Glioma International Consortium in 2007 to address the role of inherited susceptibility. Since this study does not allow us currently to address the role of genetic susceptibility in sporadic cases, we propose this case- control study to address this gap. Members of the consortium have conducted genome wide association studies (GWAS) that will be the discovery phase (phase I). While of high value for discovery, we propose to recruit 6,000 newly diagnosed glioma cases and 6,000 age-gender-ethnicity-matched controls, and collect detailed epidemiologic data and biologic samples from the 13 participating Gliogene sites. We hypothesize that novel inherited variation influencing the susceptibility to glioma can be identified by high density SNP analysis, and propose the following specific aims: SA1: Identify common genetic variants contributing to the risk of glioma. We plan to conduct phases 2 and 3 of the 3 phase approach, specifically we will: (SA1a) Validate GWAS findings from the discovery sets, and replicate the top hits (~23,000 SNPs) with lowest p- values from the two phase GWA studies in a series of 2,000 cases and 2,000 controls (phase 2, validation). Validate the top hits from the test set (aim 1a). From stage 2, genes with SNPs displaying an association at pd10-4 (including the complete haplotype tagging - approximately 100 SNPs) will then be genotyped in a further series of 4,000 cases and 4,000 controls (stage 3, validation). SNPs significant at 10-7 or better will be considered in the final analyses. (SA1b). Using an a priori hypothesis that DNA repair in inflammation contribute to glioma risk, we will selectively interrogate DNA repair and inflammation pathway genes using data for select genes derived from the high-density array. We will conduct a hypothesis driven genetic association in DNA repair and in inflammation/immune function related genes with glioma risk using functional genomic and bioinformatic tools to interrogate available databases. (SA2): Evaluate gene-gene (G-G) and gene- environmental (G-E) interactions with strong biologic relevance to identify G-G and G-E interactions for glioma risk using machine-learning tools (MDR, FITF, and CART) in these exploratory analyses. There are no existing glioma case-control studies with sufficient numbers of biological samples and common epidemiologic data to conduct a comprehensive validation and replication study, or to address the issues of tumor heterogeneity as we will in this study.